The initial CUT model includes ResNet-based generator [1]} with 9 residual blocks, PatchGAN discriminator [2]}, Least SquareGAN loss [3]}, batch size of 1 and Adam optimizer [4]} with learning rate of 0.0002 - this is in-line with the original settings provided in Contrastive Learning for Unpaired Image-to-Image Translation [5]} paper.
The reason we choose such parameters is to be as close as possible to CycleGAN [6]}.
The only difference is is that the \(\ell _1\)  cycle-consistency lossforward cycle-consistency loss: \(x \rightarrow G(x) \rightarrow F(G(x)) \approx x\)
   backward cycle-consistency loss: \(y \rightarrow F(y) \rightarrow G(F(y)) \approx y\)
 ([6]}) is replaced with contrastive loss [5]}.
